Dissecting Neurocognitive Components of Compulsivity Using Computational Modeling and EEG

Icahn School Of Medicine At Mount Sinai, New York NY

Investigators

Abstract

PROJECT SUMMARY/ABSTRACT Compulsivity is a transdiagnostic maladaptive behavior implicated in several psychiatric illnesses including obsessive-compulsive disorder (OCD)—the prototypical compulsive disorder that affects ~1.5% of people worldwide. OCD is highly disabling, and gold-standard treatments like exposure and response prevention (EX/RP) will result in remission for fewer than 50% of patients. Greater mechanistic understanding of compulsivity could improve clinical outcomes and lead to novel targets for treatments and early interventions. Thus, the goal of this K23 Award is to promote Dr. Rapp’s development into an independent clinical scientist who uses precision analytic methods to advance understanding of neurocognitive mechanisms underlying OCD and related anxiety disorders and translates these findings into innovative diagnostics and treatments. Specifically, Dr. Rapp’s training plan will capitalize on a multidisciplinary mentorship team and an outstanding research environment to enable her to gain expertise in: 1) methods and frameworks for studying transdiagnostic brain-behavior associations that can be applied to research on compulsivity, 2) theory-driven cognitive computational modeling, 3) trial-by-trial EEG analysis, 4) repeated-measures study design and analysis, and 5) career development toward research independence. The proposed research project will use theory-driven computational modeling together with trial-by-trial analysis of EEG to examine the neurocognitive mechanisms underlying compulsivity and will provide hands-on experience to foster these training goals. Prior theories of compulsivity purport that it is attributable to an imbalance between goal-directed and habitual behaviors, which are putatively underpinned by the “model-based” (MB) and “model-free” (MF) cognitive systems. Research in healthy individuals using computational modeling together with EEG has challenged this binary “dual-systems” theory, revealing more complex interactions of neurocognitive subcomponents. Particularly, these studies have shown that neurocognitive processes that support MB planning are combined with MF learning signals to influence reward prediction errors, which are used to adaptively guide behavior. These findings lead to a novel hypothesis that compulsivity results not from an imbalance in MB and MF learning systems, but rather an alteration in their integration. The proposed K23 project will test this hypothesis for the first time. Fifty unmedicated adults with OCD and 50 healthy controls will complete two reinforcement learning tasks while EEG is recorded at the start and end of a 10-week period during which participants with OCD will receive a standard course of EX/RP, the central mechanism of which is learning from prediction errors. The combination of theory- driven computational modeling and trial-by-trial EEG analysis will be used to reveal temporally precise neural dynamics of MB-MF integration and link this information with clinically-relevant outcomes. This K23 will provide key training and preliminary data for a future R01 grant, launching Dr. Rapp to independence.